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作为一种通过获取人面部的特征信息进行身份确认的技术，人脸识别近年来一直是人工智能、计算机视觉、心理学等领域的热门研究问题。区别于指纹、虹膜等其他生物识别，人脸识别技术具有直观、非接触性、方便采集、交互性强、可扩展性的优点［1］，人脸识别被广泛应用于门禁考勤、访问控制、欺诈检测、公安刑侦、智能支付等领域。人脸技术虽然取得了一些研究成果，但由于人脸的不同姿态、表情以及光线、遮挡、角度、分辨率等因素，识别率和准确性会降低，影响了人脸识别的研究效果。目前，传统的人脸识别技术已经不能解决各类扭王块模具 高铁遮板模具  保定室内设计培训 保定平面设计培训复杂因素下的人脸识别问题，实际应用面临诸多挑战。随着深度学习的发展和广泛应用，人脸识别研究有了很大的突破，其自身适应性、精确性和智能度得到很大提升
As a technology of identity recognition by obtaining the characteristic information of human face, face recognition has been a hot research problem in the fields of artificial intelligence, computer vision, psychology and so on. Different from other biometrics such as fingerprint and iris, face recognition technology has the advantages of intuition, non-contact, convenient collection, strong interaction and scalability [1]. Face recognition is widely used in access control and attendance, access control, fraud detection, public security criminal investigation, intelligent payment and other fields. Although some research achievements have been made in face technology, the recognition rate and accuracy will be reduced due to the different posture, expression, light, occlusion, angle, resolution and other factors, which affects the research effect of face recognition. At present, the traditional face recognition technology can not solve the problem of face recognition under various complex factors, and the practical application faces many challenges. With the development and wide application of deep learning, the research of face recognition has made a great breakthrough, and its adaptability, accuracy and intelligence have been greatly improved